Compared with image classification, OOD detection in semantic image segmentation has received much less attention. A recent study found that methods proposed for OOD detection in image classification do not translate well to image segmentation tasks [1]}. For semantic segmentation of street view images, one study proposed a dedicated neural network to detect OOD data [2]}. Their approach aims to classify an image as in-distribution or OOD using a very large “background dataset" to represent the distribution of the variety of visual scenes outside of the training data distribution. The authors use the ILSVRC dataset as the background dataset. However, it is difficult to obtain or even define the background set, especially in medical imaging. One study used prediction uncertainty measures to identify OOD data in medical image segmentation [3]}. However, they evaluated this method on data that were hard to segment, not on true OOD data. As we show in Section  of this paper, methods based on prediction uncertainty cannot accurately detect OOD data.
